Beta-2 microglobulin as a biomarker for peripheral artery disease

ABSTRACT

The present invention provides β2 microglobulin as a biomarker for qualifying or assessing peripheral artery disease in a subject.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 60/781,403, filed Mar. 11, 2006, and U.S. Provisional PatentApplication Ser. No. 60/863,951, filed Nov. 1, 2006, the disclosures ofwhich are incorporated herein by reference in their entirety.

FIELD OF THE INVENTION

The invention relates generally to clinical diagnostics.

BACKGROUND OF THE INVENTION

Atherosclerosis is the accumulation of lipid-fibrin plaques on theluminal wall of vascular endothelial cells. The presence ofatherosclerotic plaques can severely diminish vascular flow to targetorgans, leading to morbidity and mortality. Atherosclerotic plaques mayoccur in coronary arteries (coronary artery disease, “CAD”, which cancause angina and heart attacks), in carotid arteries (carotid arterydisease, which can cause stroke), and in arteries of the limb (usuallyaffecting the leg arteries, also known as peripheral artery disease,“PAD”). Individuals may have narrowings in one or more of these regions.There are approximately 15 million individuals in the US with CAD; 8million people with PAD; and about 5 million people with carotid arterydisease. Whereas carotid and coronary artery disease are usuallyrecognized by physicians, the diagnosis of PAD is usually missed.

The PARTNERS trial was a recent screening study which examined theprevalence of PAD in smokers or diabetics over the age of 55, or anyindividual over the age of 70, which were visiting their primarypractitioner for a routine visit (Hirsch AT et al., “Peripheral arterialdisease detection, awareness, and treatment in primary care,” JAMA, 286:1317-24 (2001)). In over 7000 patients that were screened, over 25% werefound to have PAD, as detected by an ankle pressure measurements.Unfortunately, only ⅓ of these patients had previously been diagnosed.The majority had been unrecognized by their doctors as having PAD. PADis commonly under-diagnosed and under-treated in part because manypatients do not manifest the classic symptomatology. Exertional leg painrelieved by rest is only noted by 10-30% of patients (Hirsch et al.,above). As a consequence, appropriate treatment for atherosclerosis isnot initiated in many of these patients.

Because PAD patients are underdiagnosed and undertreated, they are athigher risk for cardiovascular death. Untreated PAD can lead todecreased mobility, ulcers, gangrene, and may ultimately requireamputation of the affected extremity. Patients with PAD are at increasedrisk from myocardial infarction, cerebrovascular attack, aortic aneurymrupture, and vascular death (Criqui M H et al, “The epidemiology ofperipheral arterial disease: importance of identifying the population atrisk,” Vasc Med., 2:221-6 (1997); Meijer W T et al., “Peripheralarterial disease in the elderly: The Rotterdam Study,” ArteriosclerThromb Vasc Biol., 18:185-92 (1998)).

A useful screening test for PAD is the ankle-brachial index (“ABI”). TheABI requires that the blood pressure be taken at the arm, and at theankle. One calculates the ratio of the systolic pressure in the lowerextremity to that in the upper extremity. In most healthy individuals,the ratio is close to 1 (i.e., 0.90 or greater) while in patients with aratio less than 0.90, PAD is diagnosed. Generally, the lower the ratio,the more severe the disease. To assess the pressure at the ankle, oneneeds to use special equipment, i.e., a Doppler ultrasound probe. Asimple stethoscope will not suffice because the leg vessels of adultstend to be stiffer than those in the arm, and do not generate Korotkoffsounds during deflation of the blood pressure cuff. Unfortunately, theDoppler ultrasound equipment requires special training, and is not usedin the offices of primary practitioners. Accordingly, PAD is usually notdiagnosed. Moreover, in patients with diabetes, who constitute over 30%of patients with PAD, poor vascular compressibility may cause the ABItest to yield false negatives.

PAD, when diagnosed early, is amenable to treatments which slowprogression of the disease. Also, medications known to prevent heartattacks and strokes in patients with atherosclerosis (e.g.,anti-platelet agents, statins, angiotensin converting enzyme inhibitors)are underutilized in PAD patients. Therefore, a need exists forscreening tests which will alert the clinician to the possibility thattheir patient may have PAD. In particular, a blood test for PAD would behelpful since it could be performed in a routine clinical setting.

SUMMARY OF THE INVENTION

The present invention provides, in one embodiment, a method forqualifying or assessing the risk for peripheral artery disease in asubject comprising measuring β2-microglobulin in a biological samplefrom the subject. In a related embodiment, one (a) measuresβ2-microglobulin in a biological sample from the subject, and (b)correlates the measurement or measurements with peripheral arterydisease versus non-peripheral artery disease. In another relatedembodiment, the sample is a blood or blood derivative sample. In yetanother related embodiment, the levels in the sample of one or morebiomarkers in addition to β2-microglobulin are measured. In anotherrelated embodiment, the additionally measured biomarkers are cystatin Cor lysozyme or both. In another related embodiment, the beta2-microglobulin, cystatin C or lysozyme measurements are obtained by animmuno assay.

In another embodiment of the method for qualifying peripheral arterydisease in a subject, cystatin C or lysozyme or both are measured in abiological sample from the subject, and the measurement is correlatedwith peripheral artery disease status.

In another embodiment of the method for qualifying peripheral arterydisease status in a subject, the β2-microglobulin in the sample ismeasured by mass spectrometry. In yet another related embodiment, themass spectrometry method is SELDI-MS.

In another embodiment of the method for qualifying peripheral arterydisease status in a subject, β2-microglobulin in a sample from thesubject is measured by a method other than mass spectrometry, such as animmunoassay.

In another embodiment of the method for qualifying or assessing the riskof peripheral artery disease status in a subject, the method comprisescorrelating the measured levels of β2-microglobulin in the subject byexecuting a software classification algorithm. In another embodiment,the method for qualifying peripheral artery disease status furthercomprises the step of reporting the status to the subject. In anotherembodiment, the method further comprises recording the status on atangible medium. In yet another embodiment, the method further comprisesmanaging subject treatment based on the subject's peripheral arterydisease status. In yet another embodiment, the method further comprisesmeasuring at least one biomarker after subject management andcorrelating the measurement with disease progression.

In another embodiment, the invention provides a method for determiningthe course of peripheral artery disease comprising (a) measuring, at afirst time, β2-microglobulin in a biological sample from the subject;(b) measuring, at a second time, β2-microglobulin in a biological samplefrom the subject; and (c) comparing the first measurement and the secondmeasurement, wherein the comparative measurements determine the efficacyof treatment for peripheral artery disease.

In another embodiment, the invention provides a kit comprising (a) asolid support comprising at least one capture reagent attached thereto,wherein the capture reagent binds β2-microglobulin; and (b) instructionsfor using the solid support to detect β2-microglobulin. In a relatedembodiment, the solid support comprising a capture reagent is a SELDIprobe. In another related embodiment, the kit further comprises astandard reference of β2-microglobulin.

In another embodiment, the invention provides a software productcomprising (a) code that accesses data attributed to a sample, where thedata comprises a measurement of at least one biomarker in the sample,wherein at least one biomarker is beta-2-microglobulin; and (b) codethat executes a classification algorithm that classifies the peripheralartery disease status of the sample as a function of the measurement.

Another embodiment of the invention provides a method comprisingcommunicating to a subject a diagnosis relating to peripheral arterydisease status determined from the correlation of at least one biomarkerin a sample from the subject, wherein at least one biomarker isbeta-2-microglobulin. In a related embodiment, the beta 2-microglobulinmeasurements are obtained by an immunoassay. In another relatedembodiment, the method the diagnosis is communicated to the subject viaa computer-generated medium.

In yet another embodiment, the invention provides a method forqualifying peripheral artery disease status in a subject comprising (a)measuring at least one biomarker in a biological sample from thesubject, wherein said at least one biomarker is selected from the groupconsisting of β2-microglobulin, lysozyme and cystatin C; and (b) furthermeasuring one or more of the following criteria of said subject:C-reactive protein levels, total cholesterol levels, triglyceridelevels, low density lipoprotein levels, high density lipoprotein levels,blood sugar, blood pressure, homocysteine levels, the ankle brachialindex, interleukin levels, fibrinogen levels, lipoprotein(a) levels,8-iso-prostaglandin F 2alpha (8-iso-PGF 2alpha), and soluble Fas levels;and (c) correlating said measurements (a) and (b) with peripheral arterydisease versus non-peripheral artery disease. In certain embodiments,the correlating step will include entering one or more of theabove-mentioned values into an algorithm that can then predict the riskof the individual having peripheral arterial disease. In another relatedembodiment, the beta 2-microglobulin, cystatin C or lysozymemeasurements are obtained by an immunoassay.

In another embodiment, the invention provides a method for predicting asubject's responsiveness to a therapeutic regimen for treatingperipheral artery disease, comprising: (a) first measuring at least onebiomarker in a biological sample from the subject, wherein said at leastone biomarker is selected from the group consisting of β2-microglobulin,lysozyme and cystatin C; and (b) after said first measuring step,administering an initial treatment in a therapeutic regimen for treatingperipheral artery disease; and (c) after said treatment, measuring saidat least one biomarker a second time; and (d) comparing said first andsecond measurements, wherein decreasing levels of said at least onebiomarker correlate with an increased likelihood of a subject'sresponsiveness to said therapeutic regimen. In a related embodiment, thebeta 2-microglobulin, cystatin C or lysozyme measurements are obtainedby an immunoassay.

The invention additionally provides a method for identifying a compoundthat interacts with beta-2-microglobulin, wherein said method comprises(a) contacting beta-2-microglobulin with a test compound; and (b)determining whether the test compound interacts withbeta-2-microglobulin.

Other preferred embodiments are described elsewhere herein and in theClaims. Additional features, objects and advantages of the invention andits preferred embodiments will become apparent from the detaileddescription, examples and claims that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a table summarizing patients used in a screening study forPAD biomarkers. CAD, coronary artery disease; HTN, hypertension; ABI,ankle-brachial index; BMI, body mass index. The average measured ABI forPAD patients in the discovery study was 0.60±0.18; the average measuredABI for control patients was 1.06±0.10.

FIG. 2 shows results of an analysis of SELDI-TOF mass spectra usingplasma samples obtained from patients described in FIG. 1. The data wasanalyzed with Statistical Analysis of Microarrays (SAM) software.

FIG. 3 shows a table summarizing data relating to the 11 proteinsidentified as significant in FIG. 2, using the SAM analysis. Thisanalysis was confirmed by a second bioinformatics approach (PredictionAnalysis for Microarrays or “PAM” (Tibshurani et al., Proc. Natl. Acad.Sci. USA (2002) 99:6567-72)).

FIG. 4 shows results of a Western blot of beta 2-microglobulinconcentrations in samples from 4 patients with PAD compared to samplesfrom 4 control subjects.

FIG. 5 shows a table summarizing the two groups of patients used in thevalidation study (see, Example 2, herein, for more detail). GFR,glomerular filtration rate.

FIG. 6 shows results of a Mann-Whitney nonparametric statisticalanalysis of plasma and serum beta 2-microglobulin levels in PAD andcontrol subjects, as measured using an ELISA assay.

FIG. 7 shows the results of a Spearman's Rank Correlation analysis ofthe relationship between patients' ABI measurements and beta2-microglobulin levels as measured by ELISA.

FIG. 8 shows the results of a linear regression analyses ofELISA-measured levels of beta 2-microglobulin and other patient traits.

FIG. 9 shows the results of a multivariate model created to assess theindependent relationship between log beta 2-microglobulin and ABI.

FIG. 10 shows ACT (average claudication time) compared to total peakintensities of the six beta-2-microglobulin proteins in subjects with orwithout PAD. Control, n=43, i.e., no claudication during exercise;PAD(LC)>12 minutes, n=6; PAD<12 minutes, n=39. Total peak intensitiesare represented in arbitrary units. The mean difference is significantat the p<0.05 level by Bonferroni multiple comparison. (*) representdifferences between groups by one-way ANOVA with Bonferroni correction,with p values P<0.05. Error bars show standard deviations.

DETAILED DESCRIPTION OF THE INVENTION 1. Introduction

A biomarker is an organic biomolecule which is differentially present ina sample taken from a subject of one phenotypic status (e.g., having adisease) as compared with another phenotypic status (e.g., not havingthe disease). A biomarker is differentially present between differentphenotypic statuses if the mean or median expression level of thebiomarker in the different groups is calculated to be statisticallysignificant. Common tests for statistical significance include, amongothers, t-test, ANOVA, Kruskal-Wallis, Wilcoxon, Mann-Whitney and oddsratio. Biomarkers, alone or in combination, provide measures of relativerisk that a subject belongs to one phenotypic status or another. Assuch, they are useful as markers for disease (diagnostics), therapeuticeffectiveness of a drug (theranostics) and of drug toxicity.

The biomarker of this invention were discovered, in part, using SELDI.Accordingly, they are characterized, in part, by their mass-to-chargeratio, the shape of the peak in a mass spectrum and their bindingcharacteristics. These characteristics represent inherentcharacteristics of the biomolecule and not process limitations in themanner in which the biomolecule is discriminated.

The biomarker of this invention is characterized in part by theirmass-to-charge ratio. The mass-to-charge ratio of each biomarker isprovided herein. A particular molecular marker designated, for example,as “M11.7K” has a measured mass-to-charge ratio of 11.7K D. Themass-to-charge ratios were determined from mass spectra generated on aCiphergen Biosystems, Inc. PBS II mass spectrometer or a Ciphergen PCS4000 mass spectrometer. The PBS II is instrument has a mass accuracy ofabout +/−0.15 percent. Additionally, the instrument has a massresolution of about 400 to 1000 m/dm, where m is mass and dm is the massspectral peak width at 0.5 peak height. The PCS4000 instrument has amass accuracy of about +/−0.12% raw data with an expected externallycalibrated mass accuracy of 0.1% and internally calibrated mass accuracyof 0.01%. Additionally, the instrument has a mass resolution of about1000 to 2000 m/dm, where m is mass and dm is the mass spectral peakwidth at 0.5 peak height. The mass-to-charge ratio of the biomarkers wasdetermined using Biomarker Wizard™ software (Ciphergen Biosystems,Inc.). Biomarker Wizard assigns a mass-to-charge ratio to a biomarker byclustering the mass-to-charge ratios of the same peaks from all thespectra analyzed, as determined by the PBSII or PCS4000, taking themaximum and minimum mass-to-charge-ratio in the cluster, and dividing bytwo. Accordingly, the masses provided reflect these specifications.

The biomarker of this invention may be further characterized by theshape of its spectral peak in time-of-flight mass spectrometry.

The biomarker of this invention is also characterized by its bindingcharacteristics to adsorbent surfaces. The binding characteristics ofthe biomarker are also described herein.

2. Biomarker for Peripheral Artery Disease

2.1. β-2 microglobulin

We have discovered that β2-microglobulin is useful as a biomarker forperipheral artery disease. The mass of β2-microglobulin corresponds to a11.7K Dalton biomarker for peripheral artery disease described inInternational Patent Publication WO 2005/121758 A1 (Fung et al.).β2-microglobulin is a 99 amino acid protein derived from a 119 aminoacid precursor (GI: 179318; SwissProt Accession No. P61769).β2-microglobulin is recognized by antibodies available from, e.g., Abcam(catalog AB759) (www.abcam.com, Cambridge, Mass.). Specifics of theβ2-microglobulin biomarker are presented in Table 1, Table 2 and FIG. 3.The fractions referred to in Table 1 are the fractions in which thebiomarker elutes from the QHyper DF column described in Example 1. TABLE1 Up or down regulated in peripheral Marker P-Value artery diseaseProteinChip ® assay β2-microglobulin <0.05 Up IMAC-Cu⁺⁺ (M11.7K)(fractions 1-3) (predicted mass: <0.05 Up CM10 11,729.17 D) (fraction 2)Cystatin C <0.05 Up CM10 (predicted mass: (fraction 1) 13,343 D)Lysozyme <0.05 Up CM10 (predicted mass: (fraction 1) 14,692 D)

3. Biomarkers and Different Forms of a Protein

Proteins frequently exist in a sample in a plurality of different forms.These forms can result from either or both of pre- andpost-translational modification. Pre-translational modified formsinclude allelic variants, splice variants and RNA editing forms.Post-translationally modified forms include forms resulting fromproteolytic cleavage (e.g., fragments of a parent protein),glycosylation, phosphorylation, lipidation, oxidation, methylation,cysteinylation, sulphonation and acetylation. When detecting ormeasuring a protein in a sample, the ability to differentiate betweendifferent forms of a protein depends upon the nature of the differenceand the method used to detect or measure. For example, an immunoassayusing a monoclonal antibody (e.g., a monoclonal antibody which binds toan epitope of beta 2-microglobulin) will detect all forms of a proteincontaining the epitope and will not distinguish between them. However, asandwich immunoassay that uses two antibodies directed against differentepitopes on a protein will detect all forms of the protein that containboth epitopes and will not detect those forms that contain only one ofthe epitopes. In diagnostic assays, the inability to distinguishdifferent forms of a protein has little impact when the forms detectedby the particular method used are equally good biomarkers as anyparticular form. However, when a particular form (or a subset ofparticular forms) of a protein is a better biomarker than the collectionof different forms detected together by a particular method, the powerof the assay may suffer. In this case, it is useful to employ an assaymethod that distinguishes between forms of a protein and thatspecifically detects and measures a desired form or forms of theprotein. Distinguishing different forms of an analyte or specificallydetecting a particular form of an analyte is referred to as “resolving”the analyte.

Mass spectrometry is a particularly powerful methodology to resolvedifferent forms of a protein because the different forms typically havedifferent masses that can be resolved by mass spectrometry. Accordingly,if one form of a protein is a superior biomarker for a disease thananother form of the biomarker, mass spectrometry may be able tospecifically detect and measure the useful form where traditionalimmunoassay fails to distinguish the forms and fails to specificallydetect to useful biomarker.

One useful methodology combines mass spectrometry with immunoassay.First, a biosepcific capture reagent (e.g., an antibody, aptamer orAffibody that recognizes the biomarker and other forms of it) is used tocapture the biomarker of interest. Preferably, the biospecific capturereagent is bound to a solid phase, such as a bead, a plate, a membraneor a chip. After unbound materials are washed away, the capturedanalytes are detected and/or measured by mass spectrometry. (This methodalso will also result in the capture of protein interactors that arebound to the proteins or that are otherwise recognized by antibodies andthat, themselves, can be biomarkers.) Various forms of mass spectrometryare useful for dectecting the protein forms, including laser desorptionapproaches, such as traditional MALDI or SELDI, and electrosprayionization.

Thus, when reference is made herein to detecting a particular protein orto measuring the amount of a particular protein, it means detecting andmeasuring the protein with or without resolving various forms ofprotein. For example, the step of “measuring beta-2-microglobulin”includes measuring beta-2-microglobulin by means that do notdifferentiate between various forms of the protein (e.g., certainimmunoassays) as well as by means that differentiate some forms fromother forms or that measure a specific form of the protein. In contrast,when it is desired to measure a particular form or forms of a protein,e.g., a particular form of beta-2-microglobulin, the particular form (orforms) is specified. For example, “measuring beta-2-microglobulin(M11.7K)” means measuring beta-2-microglobulin M11.7K in a way thatdistinguishes it from other forms of beta-2-microglobulin.

4. Detection of Beta 2-microglobulin

The β2-microglobulin, cystatin C and lysozyme biomarkers of the presentinvention can be detected by any suitable method. Detection paradigmsinclude optical methods, electrochemical methods (voltametry andamperometry techniques), atomic force microscopy, and radio frequencymethods, e.g., multipolar resonance spectroscopy. Illustrative ofoptical methods, in addition to microscopy, both confocal andnon-confocal, are detection of fluorescence, luminescence,chemiluminescence, absorbance, reflectance, transmittance, andbirefringence or refractive index (e.g., surface plasmon resonance,ellipsometry, a resonant mirror method, a grating coupler waveguidemethod or interferometry).

In one embodiment, a sample is analyzed by means of a biochip. A biochipgenerally comprises a solid substrate having a substantially planarsurface, to which a capture reagent (also called an adsorbent oraffinity reagent) is attached. Frequently, the surface of a biochipcomprises a plurality of addressable locations, each of which has thecapture reagent bound there.

Protein biochips are biochips adapted for the capture of polypeptides.Many protein biochips are described in the art. These include, forexample, protein biochips produced by Ciphergen Biosystems, Inc.(Fremont, Calif.), Zyomyx (Hayward, Calif.), Invitrogen (Carlsbad,Calif.), Biacore (Uppsala, Sweden) and Procognia (Berkshire, UK).Examples of such protein biochips are described in the following patentsor published patent applications: U.S. Pat. No. 6,225,047 (Hutchens &Yip); U.S. Pat. No. 6,537,749 (Kuimelis and Wagner); U.S. Pat. No.6,329,209 (Wagner et al.); PCT International Publication No. WO 00/56934(Englert et al.); PCT International Publication No. WO 03/048768(Boutell et al.) and U.S. Pat. No. 5,242,828 (Bergstrom et al.).

4.1. Detection by Mass Spectrometry

In a preferred embodiment, the biomarker of this invention is detectedby mass spectrometry, a method that employs a mass spectrometer todetect gas phase ions. Examples of mass spectrometers aretime-of-flight, magnetic sector, quadrupole filter, ion trap, ioncyclotron resonance, electrostatic sector analyzer and hybrids of these.

In a further preferred method, the mass spectrometer is a laserdesorption/ionization mass spectrometer. In laser desorption/ionizationmass spectrometry, the analytes are placed on the surface of a massspectrometry probe, a device adapted to engage a probe interface of themass spectrometer and to present an analyte to ionizing energy forionization and introduction into a mass spectrometer. A laser desorptionmass spectrometer employs laser energy, typically from an ultravioletlaser, but also from an infrared laser, to desorb analytes from asurface, to volatilize and ionize them and make them available to theion optics of the mass spectrometer. The analyis of proteins by LDI cantake the form of MALDI or of SELDI

4.1.1. SELDI

A preferred mass spectrometric technique for use in the invention is“Surface Enhanced Laser Desorption and Ionization” or “SELDI,” asdescribed, for example, in U.S. Pat. No. 5,719,060 and No. 6,225,047,both to Hutchens and Yip. This refers to a method ofdesorption/ionization gas phase ion spectrometry (e.g., massspectrometry) in which an analyte (here, one or more of the biomarkers)is captured on the surface of a SELDI mass spectrometry probe.

SELDI also has been called is called “affinity capture massspectrometry” or “Surface-Enhanced Affinity Capture” (“SEAC”). Thisversion involves the use of probes that have a material on the probesurface that captures analytes through a non-covalent affinityinteraction (adsorption) between the material and the analyte. Thematerial is variously called an “adsorbent,” a “capture reagent,” an“affinity reagent” or a “binding moiety.” Such probes can be referred toas “affinity capture probes” and as having an “adsorbent surface.” Thecapture reagent can be any material capable of binding an analyte. Thecapture reagent is attached to the probe surface by physisorption orchemisorption. In certain embodiments the probes have the capturereagent already attached to the surface. In other embodiments, theprobes are pre-activated and include a reactive moiety that is capableof binding the capture reagent, e.g., through a reaction forming acovalent or coordinate covalent bond. Epoxide and acyl-imidizole areuseful reactive moieties to covalently bind polypeptide capture reagentssuch as antibodies or cellular receptors. Nitrilotriacetic acid andiminodiacetic acid are useful reactive moieties that function aschelating agents to bind metal ions that interact non-covalently withhistidine containing peptides. Adsorbents are generally classified aschromatographic adsorbents and biospecific adsorbents.

“Chromatographic adsorbent” refers to an adsorbent material typicallyused in chromatography. Chromatographic adsorbents include, for example,ion exchange materials, metal chelators (e.g., nitrilotriacetic acid oriminodiacetic acid), immobilized metal chelates, hydrophobic interactionadsorbents, hydrophilic interaction adsorbents, dyes, simplebiomolecules (e.g., nucleotides, amino acids, simple sugars and fattyacids) and mixed mode adsorbents (e.g., hydrophobicattraction/electrostatic repulsion adsorbents).

“Biospecific adsorbent” refers to an adsorbent comprising a biomolecule,e.g., a nucleic acid molecule (e.g., an aptamer), a polypeptide, apolysaccharide, a lipid, a steroid or a conjugate of these (e.g., aglycoprotein, a lipoprotein, a glycolipid, a nucleic acid (e.g.,DNA)-protein conjugate). In certain instances, the biospecific adsorbentcan be a macromolecular structure such as a multiprotein complex, abiological membrane or a virus. Examples of biospecific adsorbents areantibodies, receptor proteins and nucleic acids. Biospecific adsorbentstypically have higher specificity for a target analyte thanchromatographic adsorbents. Further examples of adsorbents for use inSELDI can be found in U.S. Pat. No. 6,225,047. A “bioselectiveadsorbent” refers to an adsorbent that binds to an analyte with anaffinity of at least 10⁻⁸ M.

Protein biochips produced by Ciphergen Biosystems, Inc. comprisesurfaces having chromatographic or biospecific adsorbents attachedthereto at addressable locations. Ciphergen ProteinChip® arrays includeNP20 (hydrophilic); H4 and H50 (hydrophobic); SAX-2, Q-10 and (anionexchange); WCX-2 and CM-10 (cation exchange); IMAC-3, IMAC-30 andIMAC-50 (metal chelate); and PS-10, PS-20 (reactive surface withacyl-imidizole, epoxide) and PG-20 (protein G coupled throughacyl-imidizole). Hydrophobic ProteinChip arrays have isopropyl ornonylphenoxy-poly(ethylene glycol)methacrylate functionalities. Anionexchange ProteinChip arrays have quaternary ammonium functionalities.Cation exchange ProteinChip arrays have carboxylate functionalities.Immobilized metal chelate ProteinChip arrays have nitrilotriacetic acidfunctionalities (IMAC 3 and IMAC 30) orO-methacryloyl-N,N-bis-carboxymethyl tyrosine functionalities (IMAC 50)that adsorb transition metal ions, such as copper, nickel, zinc, andgallium, by chelation. Preactivated ProteinChip arrays haveacyl-imidizole or epoxide functional groups that can react with groupson proteins for covalent binding.

Such biochips are further described in: U.S. Pat. No. 6,579,719(Hutchens and Yip, “Retentate Chromatography,” Jun. 17, 2003); U.S. Pat.No. 6,897,072 (Rich et al., “Probes for a Gas Phase Ion Spectrometer,”May 24, 2005); U.S. Pat. No. 6,555,813 (Beecher et al., “Sample Holderwith Hydrophobic Coating for Gas Phase Mass Spectrometer,” Apr. 29,2003); U.S. Patent Publication No. U.S. 2003-0032043 A1 (Pohl andPapanu, “Latex Based Adsorbent Chip,” Jul. 16, 2002); and PCTInternational Publication No. WO 03/040700 (Um et al., “HydrophobicSurface Chip,” May 15, 2003); U.S. Patent Publication No. US2003-0218130 A1 (Boschetti et al., “Biochips With Surfaces Coated WithPolysaccharide-Based Hydrogels,” Apr. 14, 2003) and U.S. PatentPublication No. U.S. 2005-059086 A1 (Huang et al., “PhotocrosslinkedHydrogel Blend Surface Coatings,” Mar. 17, 2005).

In general, a probe with an adsorbent surface is contacted with thesample for a period of time sufficient to allow the biomarker orbiomarkers that may be present in the sample to bind to the adsorbent.After an incubation period, the substrate is washed to remove unboundmaterial. Any suitable washing solutions can be used; preferably,aqueous solutions are employed. The extent to which molecules remainbound can be manipulated by adjusting the stringency of the wash. Theelution characteristics of a wash solution can depend, for example, onpH, ionic strength, hydrophobicity, degree of chaotropism, detergentstrength, and temperature. Unless the probe has both SEAC and SENDproperties (as described herein), an energy absorbing molecule then isapplied to the substrate with the bound biomarkers.

In yet another method, one can capture the biomarkers with a solid-phasebound immuno-adsorbent that has antibodies that bind the biomarkers.After washing the adsorbent to remove unbound material, the biomarkersare eluted from the solid phase and detected by applying to a SELDI chipthat binds the biomarkers and analyzing by SELDI.

The biomarkers bound to the substrates are detected in a gas phase ionspectrometer such as a time-of-flight mass spectrometer. The biomarkersare ionized by an ionization source such as a laser, the generated ionsare collected by an ion optic assembly, and then a mass analyzerdisperses and analyzes the passing ions. The detector then translatesinformation of the detected ions into mass-to-charge ratios. Detectionof a biomarker typically will involve detection of signal intensity.Thus, both the quantity and mass of the biomarker can be determined.

4.1.2. SEND

Another method of laser desorption mass spectrometry is calledSurface-Enhanced Neat Desorption (“SEND”). SEND involves the use ofprobes comprising energy absorbing molecules that are chemically boundto the probe surface (“SEND probe”). The phrase “energy absorbingmolecules” (EAM) denotes molecules that are capable of absorbing energyfrom a laser desorption/ionization source and, thereafter, contribute todesorption and ionization of analyte molecules in contact therewith. TheEAM category includes molecules used in MALDI, frequently referred to as“matrix,” and is exemplified by cinnamic acid derivatives, sinapinicacid (SPA), cyano-hydroxy-cinnamic acid (CHCA) and dihydroxybenzoicacid, ferulic acid, and hydroxyaceto-phenone derivatives. In certainembodiments, the energy absorbing molecule is incorporated into a linearor cross-linked polymer, e.g., a polymethacrylate. For example, thecomposition can be a co-polymer of α-cyano-4-methacryloyloxycinnamicacid and acrylate. In another embodiment, the composition is aco-polymer of α-cyano-4-methacryloyloxycinnamic acid, acrylate and3-(tri-ethoxy)silyl propyl methacrylate. In another embodiment, thecomposition is a co-polymer of a-cyano-4-methacryloyloxycinnamic acidand octadecylmethacrylate (“C18 SEND”). SEND is further described inU.S. Pat. No. 6,124,137 and PCT International Publication No. WO03/64594 (Kitagawa, “Monomers And Polymers Having Energy AbsorbingMoieties Of Use In Desorption/Ionization Of Analytes,” Aug. 7, 2003).

SEAC/SEND is a version of laser desorption mass spectrometry in whichboth a capture reagent and an energy absorbing molecule are attached tothe sample presenting surface. SEAC/SEND probes therefore allow thecapture of analytes through affinity capture and ionization/desorptionwithout the need to apply external matrix. The C18 SEND biochip is aversion of SEAC/SEND, comprising a C18 moiety which functions as acapture reagent, and a CHCA moiety which functions as an energyabsorbing moiety.

4.1.3. SEPAR

Another version of LDI is called Surface-Enhanced Photolabile Attachmentand Release (“SEPAR”). SEPAR involves the use of probes having moietiesattached to the surface that can covalently bind an analyte, and thenrelease the analyte through breaking a photolabile bond in the moietyafter exposure to light, e.g., to laser light (see, U.S. Pat. No.5,719,060). SEPAR and other forms of SELDI are readily adapted todetecting a biomarker or biomarker profile, pursuant to the presentinvention.

4.1.4. MALDI

MALDI is a traditional method of laser desorption/ionization used toanalyze biomolecules such as proteins and nucleic acids. In one MALDImethod, the sample is mixed with matrix and deposited directly on aMALDI chip. However, the complexity of biological samples such as serumor urine make this method less than optimal without prior fractionationof the sample. Accordingly, in certain embodiments with biomarkers arepreferably first captured with biospecific (e.g., an antibody) orchromatographic materials coupled to a solid support such as a resin(e.g., in a spin column). Specific affinity materials that bindbeta2-microglobulin is described above. After purification on theaffinity material, the biomarkers are eluted and then detected by MALDI.

4.1.5. Other Forms of Ionization in Mass Spectrometry

In another method, the biomarkers are detected by LC-MS or LC-LC-MS.This involves resolving the proteins in a sample by one or two passesthrough liquid chromatography, followed by mass spectrometry analysis,typically electrospray ionization.

4.1.6. Data Analysis

Analysis of analytes by time-of-flight mass spectrometry generates atime-of-flight spectrum. The time-of-flight spectrum ultimately analyzedtypically does not represent the signal from a single pulse of ionizingenergy against a sample, but rather the sum of signals from a number ofpulses. This reduces noise and increases dynamic range. Thistime-of-flight data is then subject to data processing. In Ciphergen'sProteinChip® software, data processing typically includes TOF-to-M/Ztransformation to generate a mass spectrum, baseline subtraction toeliminate instrument offsets and high frequency noise filtering toreduce high frequency noise.

Data generated by desorption and detection of biomarkers can be analyzedwith the use of a programmable digital computer. The computer programanalyzes the data to indicate the number of biomarkers detected, andoptionally the strength of the signal and the determined molecular massfor each biomarker detected. Data analysis can include steps ofdetermining signal strength of a biomarker and removing data deviatingfrom a predetermined statistical distribution. For example, the observedpeaks can be normalized, by calculating the height of each peak relativeto some reference.

The computer can transform the resulting data into various formats fordisplay. The standard spectrum can be displayed, but in one usefulformat only the peak height and mass information are retained from thespectrum view, yielding a cleaner image and enabling biomarkers withnearly identical molecular weights to be more easily seen. In anotheruseful format, two or more spectra are compared, convenientlyhighlighting unique biomarkers and biomarkers that are up- ordown-regulated between samples. Using any of these formats, one canreadily determine whether a particular biomarker is present in a sample.

Analysis generally involves the identification of peaks in the spectrumthat represent signal from an analyte. Peak selection can be donevisually, but software is available, as part of Ciphergen's ProteinChip®software package, that can automate the detection of peaks. In general,this software functions by identifying signals having a signal-to-noiseratio above a selected threshold and labeling the mass of the peak atthe centroid of the peak signal. In one useful application, many spectraare compared to identify identical peaks present in some selectedpercentage of the mass spectra. One version of this software clustersall peaks appearing in the various spectra within a defined mass range,and assigns a mass (M/Z) to all the peaks that are near the mid-point ofthe mass (M/Z) cluster.

Software used to analyze the data can include code that applies analgorithm to the analysis of the signal to determine whether the signalrepresents a peak in a signal that corresponds to a biomarker accordingto the present invention. The software also can subject the dataregarding observed biomarker peaks to classification tree or ANNanalysis, to determine whether a biomarker peak or combination ofbiomarker peaks is present that indicates the status of the particularclinical parameter under examination. Analysis of the data may be“keyed” to a variety of parameters that are obtained, either directly orindirectly, from the mass spectrometric analysis of the sample. Theseparameters include, but are not limited to, the presence or absence ofone or more peaks, the shape of a peak or group of peaks, the height ofone or more peaks, the log of the height of one or more peaks, and otherarithmetic manipulations of peak height data.

4.1.7. General Protocol for SELDI Detection of Biomarkers for PeripheralArtery Disease

A preferred protocol for the detection of the biomarkers of theinvention is as follows. The biological sample to be tested, e.g.,serum, preferably is subject to pre-fractionation before SELDI analysis.This simplifies the sample and improves sensitivity. A preferred methodof pre-fractionation involves contacting the sample with an anionexchange chromatographic material, such as Q HyperD (BioSepra, SA). Thebound materials are then subject to stepwise pH elution using buffers atpH 9, pH 7, pH 5 and pH 4. The fractions in which the biomarkers areeluted are also indicated in Table 1, Table 2 (by pH) and FIG. 3.Various fractions containing the biomarker are collected.

The sample to be tested (preferably pre-fractionated) is then contactedwith an affinity capture probe comprising an cation exchange adsorbent(preferably a CM10 ProteinChip array (Ciphergen Biosystems, Inc.)) or anIMAC adsorbent (preferably an IMAC30 ProteinChip array (CiphergenBiosystems, Inc.)), again as indicated in Table 1, Table 2 and/or FIG.3. The probe is washed with a buffer that will retain the biomarkerwhile washing away unbound molecules (see Example 1, below). Thebiomarkers are detected by laser desorption/ionization massspectrometry.

Alternatively, samples may be diluted, with or without denaturing, inthe appropriate array binding buffer and bound and washed underconditions optimized for detecting each analyte.

Alternatively, if antibodies that recognize the biomarker are available,for example from Dako, U.S. Biological, Chemicon, Abcam and Genway.These can be attached to the surface of a probe, such as a pre-activatedPS10 or PS20 ProteinChip array (Ciphergen Biosystems, Inc.). Theseantibodies can capture the biomarkers from a sample onto the probesurface. Then the biomarkers can be detected by, e.g., laserdesorption/ionization mass spectrometry.

Any robot that performs fluidics operations can be used in these assays,for example, those available from Tecan or Hamilton.

4.2. Detection by Immunoassay

In another embodiment of the invention, the biomarkers of the inventionare measured by a method other than mass spectrometry or other thanmethods that rely on a measurement of the mass of the biomarker. In onesuch embodiment that does not rely on mass, beta 2-microglobulin ismeasured by immunoassay. Immunoassay requires biospecific capturereagents, such as antibodies, to capture the biomarkers. Antibodies canbe produced by methods well known in the art, e.g., by immunizinganimals with the biomarkers. Biomarkers can be isolated from samplesbased on their binding characteristics. Alternatively, if the amino acidsequence of a polypeptide biomarker is known, the polypeptide can besynthesized and used to generate antibodies by methods well known in theart. Beta 2-microglobulin antibodies and methods for detecting beta2-microglobulin using standard assays are described in the art, e.g.,Hilgert et al. (Folia Biol (Praha) (1984) 30:369-76). Examples of theuse of these antibodies to detect increased levels of beta2-microglobulin in PAD patients relative to normal patients are providedherein. Similar methods for the immunoassay detection of lysozyme andcystatin C are also known in the art.

This invention contemplates traditional immunoassays including, forexample, sandwich immunoassays including ELISA or fluorescence-basedimmunoassays, other enzyme immunoassays and western blot. Nephelometryis an assay done in liquid phase, in which antibodies are in solution.Binding of the antigen to the antibody results in changes in absorbance,which is measured. In the SELDI-based immunoassay, a biospecific capturereagent for the biomarker is attached to the surface of an MS probe,such as a pre-activated ProteinChip array. The biomarker is thenspecifically captured on the biochip through this reagent, and thecaptured biomarker is detected by mass spectrometry.

5. Determination of Subject Peripheral Artery Disease Status

The biomarkers of the invention can be used in diagnostic tests toassess peripheral artery disease status in a subject, e.g., to assessthe risk of a patient having peripheral artery disease. The phrase“peripheral artery disease status” includes any distinguishablemanifestation of the disease, including non-disease. For example,peripheral artery disease status includes, without limitation, thepresence or absence of disease (e.g., peripheral artery disease v.non-peripheral artery disease), the risk of developing disease, thestage of the disease, the progression of disease (e.g., progress ofdisease or remission of disease over time) and the effectiveness orresponse to treatment of disease.

The correlation of test results with peripheral artery disease statusinvolves applying a classification algorithm of some kind to the resultsto generate the status. The classification algorithm may be as simple asdetermining whether or not the amount of beta-2-microglobulin measuredis above or below a particular cut-off number. When multiple biomarkersor cardiovascular risk factors (e.g., the age, gender, blood pressure,blood sugar, and blood cholesterol) are used, the classificationalgorithm may be a linear regression formula. Alternatively, theclassification algorithm may be the product of any of a number oflearning algorithms described herein.

In the case of complex classification algorithms, it may be necessary toperform the algorithm on the data, thereby determining theclassification, using a computer, e.g., a programmable digital computer.In either case, one can then record the status on tangible medium, forexample, in computer-readable format such as a memory drive or disk orsimply printed on paper. The result also could be reported on a computerscreen.

5.1. Single Markers

The power of a diagnostic test to correctly predict status is commonlymeasured as the sensitivity of the assay, the specificity of the assayor the area under a receiver operated characteristic (“ROC”) curve.Sensitivity is the percentage of true positives that are predicted by atest to be positive, while specificity is the percentage of truenegatives that are predicted by a test to be negative. An ROC curveprovides the sensitivity of a test as a function of 1-specificity. Thegreater the area under the ROC curve, the more powerful the predictivevalue of the test. Other useful measures of the utility of a test arepositive predictive value and negative predictive value. Positivepredictive value is the percentage of people who test positive that areactually positive. Negative predictive value is the percentage of peoplewho test negative that are actually negative.

β2-microglobulin shows a statistical difference in different peripheralartery disease statuses. Diagnostic tests that use these biomarkersalone or in combination show a sensitivity and specificity of at least75%, at least 80%, at least 85%, at least 90%, at least 95%, at least98% and about 100%. A test that has a high sensitivity but a lowspecificity may still be useful if its negative predictive value is highenough to exclude a diagnosis of PAD. An example of a clinically veryuseful test that has high sensitivity but low specificity is theventilation-perfusion scan. A negative test virtually excludes pulmonaryembolism since the negative predictive value is over 95%. Such a testresult can reduce the need for further and more expensive testing.

β2-microglobulin, lysozyme and cystatin C are differentially present inperipheral artery disease, and, therefore, are each useful by themselvesin methods of determining peripheral artery disease status. The methodinvolves, first, measuring β2-microglobulin in a subject sample usingthe methods described herein, e.g., measurement by an immunoassay orcapture on a SELDI biochip followed by detection by mass spectrometryand, second, comparing the measurement with a diagnostic amount orcut-off that distinguishes a positive peripheral artery disease statusfrom a negative peripheral artery disease status. The diagnostic amountrepresents a measured amount of a biomarker above which or below which asubject is classified as having a particular peripheral artery diseasestatus. For example, because beta-2-microglobulin is up-regulated inperipheral artery disease compared to normal, then a measured amount ofbeta-2-microglobulin above the diagnostic cutoff indicates an increasedrisk of peripheral artery disease. By contrast, a level may be lowenough to virtually exclude PAD as a diagnosis. As is well understood inthe art, by adjusting the particular diagnostic cut-off used in anassay, one can increase sensitivity or specificity of the diagnosticassay depending on the preference of the diagnostician. The particulardiagnostic cut-off can be determined, for example, by measuring theamount of the biomarker in a statistically significant number of samplesfrom subjects with the different peripheral artery disease statuses, aswas done here, and drawing the cut-off to suit the diagnostician'sdesired levels of specificity and sensitivity.

5.2. Combinations of Markers

While individual biomarkers are useful diagnostic biomarkers, it hasbeen found that a combination of biomarkers can provide greaterpredictive value of a particular status than single biomarkers alone.Specifically, the detection of a plurality of biomarkers in a sample canincrease the sensitivity and/or specificity of the test. A combinationof at least two biomarkers is sometimes referred to as a “biomarkerprofile” or “biomarker fingerprint.” Accordingly, beta-2-microglobulincan be combined with other biomarkers for peripheral artery disease toimprove the sensitivity and/or specificity of the diagnostic test.Examples of other biomarkers useful for screening for PAD are found inthe PCT Application US2005/018728 (Inter. Pub. No. WO2005/121758), filedMay 26, 2005.

5.3. Peripheral Artery Disease Status

Determining peripheral artery disease status typically involvesclassifying an individual into one of two or more groups (statuses)based on the results of the diagnostic test. The diagnostic testsdescribed herein can be used to classify between a number of differentstates. The phrase “PAD status” includes distinguishing, inter alia, PADv. non-PAD (e.g., normal) and/or PAD v. “long claudicator” PAD (LC PAD).A long claudicator is an individual with PAD that is less limited thanother patients (i.e., the long claudicator can walk for more than 12minutes on a treadmill using the Skinner-Gardner protocol). Based onthis status, further procedures may be indicated, including additionaldiagnostic tests or therapeutic procedures or regimens.

5.3.1. Presence of Disease

In one embodiment, this invention provides methods for assessing therisk of peripheral artery disease in a subject (status: peripheralartery disease v. non-peripheral artery disease). The risk of peripheralartery disease is determined by measuring the relevant biomarker orbiomarkers and then either submitting them to a classification algorithmor comparing them with a reference amount and/or pattern of biomarkersthat is associated with the particular risk level.

5.3.2. Determining Risk of Developing Disease

In one embodiment, this invention provides methods for determining therisk of developing peripheral artery disease in a subject (status:low-risk v. high risk). Biomarker amounts or patterns are characteristicof various risk states, e.g., high, medium or low. The risk ofdeveloping a disease is determined by measuring the relevant biomarkeror biomarkers and then either submitting them to a classificationalgorithm or comparing them with a reference amount and/or pattern ofbiomarkers that is associated with the particular risk level

5.3.3. Determining Stage of Disease

In one embodiment, this invention provides methods for determining thestage of disease in a subject. Each stage of a disease will have acharacteristic amount of a biomarker or relative amounts of a set ofbiomarkers (a pattern). The stage of a disease is determined bymeasuring the relevant biomarker (e.g., beta 2-microglobulin) orbiomarkers and then either submitting them to a classification algorithmor comparing them with a reference amount and/or pattern of biomarkersthat is associated with the particular stage. For example, one canclassify between early stage peripheral artery disease andnon-peripheral artery disease.

5.3.4. Determining Course (Progression/Remission) of Disease

In one embodiment, this invention provides methods for determining thecourse of disease in a subject. Disease course refers to changes indisease status over time, including disease progression (worsening) anddisease regression (improvement). Over time, the amounts or relativeamounts (e.g., the pattern) of the biomarkers changes. For example, highbeta-2-microglobulin levels, and/or high lysozyme levels and/or highcystatin C levels are correlated with PAD. Therefore, the trend of thesemarkers, either increased or decreased over time toward diseased ornon-diseased, can be used to monitor the course of the disease.Accordingly, this method involves measuring one or more biomarkers in asubject for at least two different time points, e.g., a first time and asecond time, and comparing the change in amounts, if any. The course ofdisease is determined based on these comparisons.

5.4. Reporting the Status

Additional embodiments of the invention relate to the communication ofassay results or diagnoses or both to technicians, physicians orpatients, for example. In certain embodiments, computers will be used tocommunicate assay results or diagnoses or both to interested parties,e.g., physicians and their patients. In some embodiments, the assayswill be performed or the assay results analyzed in a country orjurisdiction which differs from the country or jurisdiction to which theresults or diagnoses are communicated.

In a preferred embodiment of the invention, a diagnosis based onβ2-microglobulin and/or lysozyme and/or cystatin C in a test subject iscommunicated to the subject after the diagnosis is obtained. Thediagnosis may be communicated to the subject by the subject's treatingphysician. Alternatively, the diagnosis may be sent to a test subject byemail or communicated to the subject by phone. A computer may be used tocommunicate the diagnosis by email or phone. In certain embodiments, themessage containing results of a diagnostic test may be generated anddelivered automatically to the subject using a combination of computerhardware and software which will be familiar to artisans skilled intelecommunications. One example of a healthcare-oriented communicationssystem is described in U.S. Pat. No. 6,283,761; however, the presentinvention is not limited to methods which utilize this particularcommunications system. In certain embodiments of the methods of theinvention, all or some of the method steps, including the assaying ofsamples, diagnosing of diseases, and communicating of assay results ordiagnoses, may be carried out in diverse (e.g., foreign) jurisdictions.

5.5. Subject Management

In certain embodiments of the methods of qualifying or assessingperipheral artery disease status, the methods further comprise managingsubject treatment based on the status. Such management includes theactions of the physician or clinician subsequent to determiningperipheral artery disease status. For example, if a physician makes adiagnosis of peripheral artery disease, then a certain regimen oftreatment may follow. A suitable regimen of treatment may include,without limitation, a supervised exercise program; control of bloodpressure, sugar intake, and/or lipid levels; cessation of smoking,including any necessary counseling and nicotine replacement; and drugtherapies including the administration of aspirin (with or withoutdipyridamole), clopidogrel, cilostazol, and/or pentoxifylline.Alternatively, a diagnosis of PAD might be followed by further testingto determine whether a patient is suffering from a specific form of PAD,or whether the patient is suffering from related diseases such ascoronary artery disease. Also, if the diagnostic test gives aninconclusive result on PAD status, further tests may be called for.

6. Generation of Classification Algorithms for Qualifying PeripheralArtery Disease Status

In some embodiments, data derived from the spectra (e.g., mass spectraor time-of-flight spectra) that are generated using samples such as“known samples” can then be used to “train” a classification model. A“known sample” is a sample that has been pre-classified. The data thatare derived from the spectra and are used to form the classificationmodel can be referred to as a “training data set.” Once trained, theclassification model can recognize patterns in data derived from spectragenerated using unknown samples. The classification model can then beused to classify the unknown samples into classes. This can be useful,for example, in predicting whether or not a particular biological sampleis associated with a certain biological condition (e.g., diseased versusnon-diseased).

The training data set that is used to form the classification model maycomprise raw data or pre-processed data. In some embodiments, raw datacan be obtained directly from time-of-flight spectra or mass spectra,and then may be optionally “pre-processed” as described above.

Classification models can be formed using any suitable statisticalclassification (or “learning”) method that attempts to segregate bodiesof data into classes based on objective parameters present in the data.Classification methods may be either supervised or unsupervised.Examples of supervised and unsupervised classification processes aredescribed in Jain, “Statistical Pattern Recognition: A Review”, IEEETransactions on Pattern Analysis and Machine Intelligence, Vol. 22, No.1, January 2000, the teachings of which are incorporated by reference.

In supervised classification, training data containing examples of knowncategories are presented to a learning mechanism, which learns one ormore sets of relationships that define each of the known classes. Newdata may then be applied to the learning mechanism, which thenclassifies the new data using the learned relationships. Examples ofsupervised classification processes include linear regression processes(e.g., multiple linear regression (MLR), partial least squares (PLS)regression and principal components regression (PCR)), binary decisiontrees (e.g., recursive partitioning processes such asCART—classification and regression trees), artificial neural networkssuch as back propagation networks, discriminant analyses (e.g., Bayesianclassifier or Fischer analysis), logistic classifiers, and supportvector classifiers (support vector machines).

A preferred supervised classification method is a recursive partitioningprocess. Recursive partitioning processes use recursive partitioningtrees to classify spectra derived from unknown samples. Further detailsabout recursive partitioning processes are provided in U.S. Pat. No.6,675,104 (Paulse et al., “Method for analyzing mass spectra”).

In other embodiments, the classification models that are created can beformed using unsupervised learning methods. Unsupervised classificationattempts to learn classifications based on similarities in the trainingdata set, without pre-classifying the spectra from which the trainingdata set was derived. Unsupervised learning methods include clusteranalyses. A cluster analysis attempts to divide the data into “clusters”or groups that ideally should have members that are very similar to eachother, and very dissimilar to members of other clusters. Similarity isthen measured using some distance metric, which measures the distancebetween data items, and clusters together data items that are closer toeach other. Clustering techniques include the MacQueen's K-meansalgorithm and the Kohonen's Self-Organizing Map algorithm.

Learning algorithms asserted for use in classifying biologicalinformation are described, for example, in PCT International PublicationNo. WO 01/31580 (Barnhill et al., “Methods and devices for identifyingpatterns in biological systems and methods of use thereof”), U.S. PatentApplication No. 2002 0193950 A1 (Gavin et al., “Method for analyzingmass spectra”), U.S. Patent Application No. 2003 0004402 A1 (Hitt etal., “Process for discriminating between biological states based onhidden patterns from biological data”), and U.S. Patent Application No.2003 0055615 A1 (Zhang and Zhang, “Systems and methods for processingbiological expression data”).

The classification models can be formed on and used on any suitabledigital computer. Suitable digital computers include micro, mini, orlarge computers using any standard or specialized operating system, suchas a Unix, Windows™ or Linux™ based operating system. The digitalcomputer that is used may be physically separate from the massspectrometer that is used to create the spectra of interest, or it maybe coupled to the mass spectrometer.

The training data set and the classification models according toembodiments of the invention can be embodied by computer code that isexecuted or used by a digital computer. The computer code can be storedon any suitable computer readable media including optical or magneticdisks, sticks, tapes, etc., and can be written in any suitable computerprogramming language including C, C++, visual basic, etc.

The learning algorithms described above are useful both for developingclassification algorithms for the biomarkers already discovered, or forfinding new biomarkers for peripheral artery disease. The classificationalgorithms, in turn, form the base for diagnostic tests by providingdiagnostic values (e.g., cut-off points) for biomarkers used singly orin combination.

7. Compositions of Matter

In another aspect, this invention provides compositions of matter basedon the biomarkers of this invention, e.g., the β2-microglobulin,lysozyme, cystatin C and other biomarkers listed in Table 1, Table 2 andFIG. 3.

In one embodiment, this invention provides the biomarker of thisinvention in purified form. Purified biomarkers have utility as antigensto raise antibodies. Purified biomarkers also have utility as standardsin assay procedures. As used herein, a “purified biomarker” is abiomarker that has been isolated from other proteins and peptides,and/or other material from the biological sample in which the biomarkeris found. The biomarkers can be isolated from biological fluids, such asurine or serum. Biomarkers may be purified using any method known in theart, including, but not limited to, mechanical separation (e.g.,centrifuigation), ammonium sulphate precipitation, dialysis (includingsize-exclusion dialysis), electrophoresis (e.g. acrylamide gelelectrophoresis) size-exclusion chromatography, affinity chromatography,anion-exchange chromatography, cation-exchange chromatography, andmethal-chelate chromatography. Such methods may be performed at anyappropriate scale, for example, in a chromatography column, or on abiochip.

In another embodiment, this invention provides a biospecific capturereagent, optionally in purified form, that specifically binds abiomarker of this invention. In one embodiment, the biospecific capturereagent is an antibody. Such compositions are useful for detecting thebiomarker in a detection assay, e.g., for diagnostics.

In another embodiment, this invention provides an article comprising abiospecific capture reagent that binds a biomarker of this invention,wherein the reagent is bound to a solid phase. For example, thisinvention contemplates a device comprising bead, chip, membrane,monolith or microtiter plate derivatized with the biospecific capturereagent. Such articles are useful in biomarker detection assays.

In another aspect this invention provides a composition comprising abiospecific capture reagent, such as an antibody, bound to a biomarkerof this invention, the composition optionally being in purified form.Such compositions are useful for purifying the biomarker or in assaysfor detecting the biomarker.

In another embodiment, this invention provides an article comprising asolid substrate to which is attached an adsorbent, e.g., achromatographic adsorbent or a biospecific capture reagent, to which isfurther bound a biomarker of this invention. In one embodiment, thearticle is a biochip or a probe for mass spectrometry, e.g., a SELDIprobe. Such articles are useful for purifying the biomarker or detectingthe biomarker.

8. Kits for Detection of Biomarkers for Peripheral Artery Disease

In another aspect, the present invention provides kits for qualifyingperipheral artery disease status, which kits are used to detectbiomarkers according to the invention. In one embodiment, the kitcomprises a solid support, such as a chip, a microtiter plate or a beador resin having a capture reagent attached thereon, wherein the capturereagent binds a biomarker of the invention. Thus, for example, the kitsof the present invention can comprise mass spectrometry probes forSELDI, such as ProteinChip® arrays. In the case of biospecific capturereagents, the kit can comprise a solid support with a reactive surface,and a container comprising the biospecific capture reagent (e.g., anantibody for beta2-microglobulin).

The kit can also comprise a washing solution or instructions for makinga washing solution, in which the combination of the capture reagent andthe washing solution allows capture of the biomarker or biomarkers onthe solid support for subsequent detection by, e.g., mass spectrometry.The kit may include more than type of adsorbent, each present on adifferent solid support.

In a further embodiment, such a kit can comprise instructions forsuitable operational parameters in the form of a label or separateinsert. For example, the instructions may inform a consumer about how tocollect the sample, how to wash the probe or the particular biomarkersto be detected.

In yet another embodiment, the kit can comprise one or more containerswith biomarker samples, to be used as standard(s) for calibration.

9. Determining Therapeutic Efficacy of Pharmaceutical Drug

In another embodiment, this invention provides methods for determiningthe therapeutic efficacy of a pharmaceutical drug. These methods areuseful in performing clinical trials of the drug, as well as monitoringthe progress of a patient on the drug. Therapy or clinical trialsinvolve administering the drug in a particular regimen. The regimen mayinvolve a single dose of the drug or multiple doses of the drug overtime. The doctor or clinical researcher monitors the effect of the drugon the patient or subject over the course of administration. If the drughas a pharmacological impact on the condition, the amounts or relativeamounts (e.g., the pattern or profile) of β2-microglobulin (or lysozymeand/or cystatin C) changes toward a non-disease profile. For example,beta-2-microglobulin is increased in patients with PAD. Therefore, onecan follow the effect of treatment (and other biomarkers) in the subjectwith PAD during the course of treatment. Accordingly, this methodinvolves measuring one or more biomarkers in a subject receiving drugtherapy, and correlating the amounts of the biomarkers with the diseasestatus of the subject. One embodiment of this method involvesdetermining the levels of the biomarkers for at least two different timepoints during a course of drug therapy, e.g., a first time and a secondtime, and comparing the change in amounts of the biomarkers, if any. Forexample, the biomarkers can be measured before and after drugadministration or at two different time points during drugadministration. The effect of therapy is determined based on thesecomparisons. If a treatment is effective, then the biomarkers will trendtoward normal, while if treatment is ineffective, the biomarkers willtrend toward disease indications. If a treatment is effective, then thebiomarkers will trend toward normal, while if treatment is ineffective,the biomarkers will trend toward disease indications.

10. Use of Biomarkers for Peripheral Artery Disease in Screening Assaysand Methods of Treating Peripheral Artery Disease

The methods of the present invention have other applications as well.For example, the biomarkers can be used to screen for compounds thatmodulate the expression of the biomarkers in vitro or in vivo, whichcompounds in turn may be useful in treating or preventing peripheralartery disease in patients. In another example, the biomarkers can beused to monitor the response to treatments for peripheral arterydisease. In yet another example, the biomarkers can be used in hereditystudies to determine if the subject is at risk for developing peripheralartery disease.

Compounds suitable for therapeutic testing may be screened initially byidentifying compounds which interact with beta-2-microglobulin and oneor more biomarkers listed herein (e.g., lysozyme or cystatin C). By wayof example, screening might include recombinantly expressing abiomarker, purifying the biomarker, and affixing the biomarker to asubstrate. Test compounds would then be contacted with the substrate,typically in aqueous conditions, and interactions between the testcompound and the biomarker are measured, for example, by measuringelution rates as a function of salt concentration. Certain proteins mayrecognize and cleave one or more biomarkers of Table 1, Table 2 or FIG.3, in which case the proteins may be detected by monitoring thedigestion of one or more biomarkers in a standard assay, e.g., by gelelectrophoresis of the proteins.

In a related embodiment, the ability of a test compound to inhibit theactivity of one or more of the biomarkers may be measured. One of skillin the art will recognize that the techniques used to measure theactivity of a particular biomarker will vary depending on the functionand properties of the biomarker. For example, an enzymatic activity of abiomarker may be assayed provided that an appropriate substrate isavailable and provided that the concentration of the substrate or theappearance of the reaction product is readily measurable. The ability ofpotentially therapeutic test compounds to inhibit or enhance theactivity of a given biomarker may be determined by measuring the ratesof catalysis in the presence or absence of the test compounds. Theability of a test compound to interfere with a non-enzymatic (e.g.,structural) function or activity of beta-2-microglobulin, lysozyme,cystatin C or another one or more of the biomarkers herein may also bemeasured. For example, the self-assembly of a multi-protein complexwhich includes beta-2-microglobulin may be monitored by spectroscopy inthe presence or absence of a test compound. Alternatively, if thebiomarker is a non-enzymatic enhancer of transcription, test compoundswhich interfere with the ability of the biomarker to enhancetranscription may be identified by measuring the levels ofbiomarker-dependent transcription in vivo or in vitro in the presenceand absence of the test compound.

Test compounds capable of modulating the activity of any of thebiomarkers of Table 1, Table 2 or FIG. 3 may be administered to patientswho are suffering from or are at risk of developing peripheral arterydisease. For example, the administration of a test compound whichincreases the activity of a particular biomarker may decrease the riskof peripheral artery disease in a patient if the activity of theparticular biomarker in vivo prevents the accumulation of proteins forperipheral artery disease. Conversely, the administration of a testcompound which decreases the activity of a particular biomarker maydecrease the risk of peripheral artery disease in a patient if theincreased activity of the biomarker is responsible, at least in part,for the onset of peripheral artery disease.

In an additional aspect, the invention provides a method for identifyingcompounds useful for the treatment of disorders such as peripheralartery disease which are associated with increased levels of modifiedforms of beta-2-microglobulin, lysozyme, or cystatin C. For example, inone embodiment, cell extracts or expression libraries may be screenedfor compounds which catalyze the cleavage of full-lengthbeta-2-microglobulin to form truncated forms of beta-2-microglobulin. Inone embodiment of such a screening assay, cleavage ofbeta-2-microglobulin may be detected by attaching a fluorophore tobeta-2-microglobulin which remains quenched when beta-2-microglobulin isuncleaved but which fluoresces when the protein is cleaved.Alternatively, a version of full-length beta-2-microglobulin modified soas to render the amide bond between amino acids x and y uncleavable maybe used to selectively bind or “trap” the cellular protease whichcleaves full-length beta-2-microglobulin at that site in vivo. Methodsfor screening and identifying proteases and their targets arewell-documented in the scientific literature, e.g., in Lopez-Ottin etal. (Nature Reviews, 3:509-519 (2002)).

In yet another embodiment, the invention provides a method for treatingor reducing the progression or likelihood of a disease, e.g., peripheralartery disease, which is associated with the increased levels oftruncated beta-2-microglobulin. For example, after one or more proteinshave been identified which cleave full-length beta-2-microglobulin,combinatorial libraries may be screened for compounds which inhibit thecleavage activity of the identified proteins. Methods of screeningchemical libraries for such compounds are well-known in art. See, e.g.,Lopez-Otin et al. (2002). Alternatively, inhibitory compounds may beintelligently designed based on the structure of beta-2-microglobulin.

Full-length beta-2-microglobulin is believed to be involved inregulation of the body's iron stores, as well as in hereditaryhemochromatosis, chronic renal insufficiency, and renal anemia.Beta-2-microglobulin expression is also induced as part of the body'simmune response via the interleuking cascade. Becausebeta-2-microglobulin is highly processed from its pre-pro and pro-forms,it is likely that there are proteases which target and cleave it.Therefore, in a further embodiment, the invention provides methods foridentifying compounds which increase the affinity of truncatedbeta-2-microglobulin for its target proteases. For example, compoundsmay be screened for their ability to cleave beta-2-microglobulin. Testcompounds capable of modulating the cleavage of beta-2-microglobulin orthe activity of molecules which interact with beta-2-microglobulin maythen be tested in vivo for their ability to slow or stop the progressionof peripheral artery disease in a subject.

At the clinical level, screening a test compound includes obtainingsamples from test subjects before and after the subjects have beenexposed to a test compound. The levels in the samples of one or more ofthe biomarkers listed in Table 1, Table 2 or FIG. 3 may be measured andanalyzed to determine whether the levels of the biomarkers change afterexposure to a test compound. The samples may be analyzed by massspectrometry, as described herein, or the samples may be analyzed by anyappropriate means known to one of skill in the art. For example, thelevels of one or more of the biomarkers listed in Table 1, Table 2, orFIG. 3 may be measured directly by Western blot using radio- orfluorescently-labeled antibodies which specifically bind to thebiomarkers. Alternatively, changes in the levels of MRNA encoding theone or more biomarkers may be measured and correlated with theadministration of a given test compound to a subject. In a furtherembodiment, the changes in the level of expression of one or more of thebiomarkers may be measured using in vitro methods and materials. Forexample, human tissue cultured cells which express, or are capable ofexpressing, one or more of the biomarkers of Table 1, Table 2 or FIG. 3may be contacted with test compounds. Subjects who have been treatedwith test compounds will be routinely examined for any physiologicaleffects which may result from the treatment. In particular, the testcompounds will be evaluated for their ability to decrease diseaselikelihood in a subject. Alternatively, if the test compounds areadministered to subjects who have previously been diagnosed withperipheral artery disease, test compounds will be screened for theirability to slow or stop the progression of the disease.

11. Examples 11.1. Example 1 Discovery of Biomarkers for PAD

The biomarkers of the present invention, including Beta 2-microglobulin,lysozyme, and cystatin C, were initially identified as a biomarker forPAD in a screening study using SELDI technology employing ProteinChiparrays from Ciphergen Biosystems, Inc. (Fremont, Calif.) (“Ciphergen”).The study set consisted of 45 patients with PAD and 43 patients withoutPAD. Subjects placed in the PAD group were those with an ankle-brachialindex of 0.9 or less. Relevant traits in these groups are summarized andcompared in FIG. 1. Patients in the PAD group were slightly older andgenerally had higher frequencies of cardiovascular risk factors.

Plasma samples were obtained from subjects in a fasting state. Eachplasma sample was subjected to fractionation on a QhyperDF column beforeanalysis using Ciphergen's ProteinChips, as described in the detailedprotocol below. After fractionation, selected fractions were analyzedusing Ciphergen's IMAC30 or CM10 ProteinChips. The spectra ofpolypeptides in the samples were generated by time-of-flight massspectrometry on a Ciphergen PBSII mass spectrometer. Peak intensityvalues (1619 peaks/sample) were analyzed by Statistical Analysis ofMicroarrays (SAM) software.

The present study differs from studies which purport to show arelationship between β₂-microglobulin levels and symptoms such asarterial stiffness (see, e.g., Saijo et al., Hypertens. Res.,28(6):505-511 (2005)). Those studies excluded from their trials subjectsdiagnosed with PDA and patients with low ABI (<0.9). Also, the studiesrelied on a pulse wave velocity (PWV) assay for including/excludingpatients. The PWV assay measures vascular compliance and not arterialdiseaseper se. While the arteries in the subjects used in Saijo et al.'strials may have been less compliant than those of a “normal” subject,they were not necessarily the narrowed and/or clogged arteries ofsubjects with PAD.

Representative protocols used for fractionation, sample handling, anddata acquisition are described below.

Q Hyper DF Anion Exchange Fractionation

Buffer List for anion exchange fractionation:

U1 (1M urea, 0.22% CHAPS, 50 mM Tris-HCl pH9)

50 mM Tris-HCl with 0.1% OGP pH9 (Wash buffer 1)

50 mM Hepes with 0.1% OGP pH7 (Wash buffer 2)

100 mM NaAcetate with 0.1% OGP pH5 (Wash buffer 3)

100 mM NaAcetate with 0.1% OGP pH4 (Wash buffer 4)

33.3% isopropanol/16.7% acetonitrile/0.1% trifluoracetic acid (Washbuffer 5)

Note: do not aliquot wash buffer 5 into the buffer tray until washbuffer 4 is being applied to the resin. This ensures that evaporation ofthe volatile organic solvents will not be an issue.

Material List:

Filter plate

5 v-well 96 well dishes, labeled F1-F5.

a. Wash Resin

Prepare resin by washing Hyper Q DF resin (BioSepra, Cergy, France) 3times with 5 bed volumes 50 mM Tris-HCl pH9. Then store in 50 mMTris-HCl pH9 in a 50% suspension.

b. Equilibrate Resin

Add 125 μL Hyper Q DF to each well in filter plate

Filter buffer

Add 150 μL U1 to each well

Filter buffer

Add 150 μL U1 to each well

Filter buffer

Add 150 μL U1 to each well

Filter buffer

c. Bind Blood Plasma with Resin

Pipet 150 μL of sample from each tube to appropriate well in filterplate

Vortex 30′ at 4°

d. Collect Fractions

Place v-well 96 well plate F1 under filter plate

Collect flow-through in plate F1

Add 100 μL of wash buffer 1 to each well of filter plate

Vortex 10′ at Room Temperature (RT)

Collect pH 9 eluant in plate F1

Fraction 1 contains the flow through and the pH 9 eluant.

Add 100 μL of wash buffer 2 to each well of filter plate

Vortex 10′ at Room Temperature (RT)

Place v-well 96 well plate F2 under filter plate

Collect fraction 2 in plate F2

Add 100 μL of wash buffer 2 to each well of filter plate

Vortex 10′ at Room Temperature (RT)

Collect remainder of fraction 2 in plate F2

Fraction 2 contains the pH 7 eluant.

Add 100 μL of wash buffer 3 to each well of filter plate

Vortex 10′ at Room Temperature (RT)

Place v-well 96 well plate F3 under filter plate

Collect fraction 3 in plate F3

Add 100 μL of wash buffer 3 to each well of filter plate

Vortex 10′ at Room Temperature (RT)

Collect remainder of fraction 3 in plate F3

Fraction 3 contains the pH 5 eluant.

Add 100 μL of wash buffer 4 to each well of filter plate

Vortex 10′ at Room Temperature (RT)

Place v-well 96 well plate F4 under filter plate

Collect fraction 4 in plate F4

Add 100 μL of wash buffer 4 to each well of filter plate

Vortex 10′ at Room Temperature (RT)

Collect remainder of fraction 4 in plate F4

Fraction 4 contains the pH 4 eluant.

Add 100 μL of wash buffer 5 to each well of filter plate

Vortex 10′ at Room Temperature (RT)

Place v-well 96 well plate F5 under filter plate

Collect fraction 5 in plate F5

Add 100 μL of wash buffer 5 to each well of filter plate

Vortex 10′ at Room Temperature (RT)

Collect remainder of fraction 5 in plate F5

Fraction 5 contains the organic solvent eluant.

Freeze until proceeding with chip binding protocol

Chip Binding Protocol

Chip Washing Buffer List:

IMAC30 array (Ciphergen Biosystems, Inc.): a suitable wash includes, butis not limited to, 50 mM Tris pH 8.0 supplemented with 500 mM NaCl.

CM10 array (Ciphergen Biosystems, Inc.): a suitable wash includes, butis not limited to, 100 mM ammonium acetate pH 4.0

Array Preparation:

Place arrays into bioprocessor

Load IMAC30 arrays with copper

Load 50 μL of CuSO₄ onto each spot of the IMAC30 array

Vortex 15′ at Room Temperature (RT)

Remove CuSO₄ and repeat.

Water rinse

Equilibrate Arrays:

Add 100 μl chip washing buffer appropriate to the array to each well

Vortex 5′ at RT

Remove buffer after vortex

Add 100 μl chip washing buffer appropriate to the array to each well

Vortex 5′ at RTk

Remove buffer after vortex

Bind Plasma Fractions from Hyper Q DF Columns to Arrays:

Add 60 μl chip washing buffer appropriate to the array to each well

Add 20 μl plasma fraction

Vortex 30′ at RT

Remove sample and buffer

Wash Arrays:

Add 100 μl chip washing buffer appropriate to the array to each well

Vortex 5′ at RT

Remove buffer after vortex

Add 100 μl chip washing buffer appropriate to the array to each well

Vortex 5′ at RT

Remove buffer after vortex

Add 100 μl chip washing buffer appropriate to the array to each well

Vortex 5′ at RT

Remove buffer after vortex

Water rinse 2 times

Add Matrix:

Remove Bioprocessor top and gasket

Allow the arrays to dry

SPA:

Add 0.8 μl 50% SPA (sinapinic acid) in 50% Acetonitrile and 0.5% TFA

Air dry

Add 0.8 μl 50% SPA

Air dry

CHCA

Add 0.8 μl 20% CHCA dissolved in 50% Acetonitrile+0.5%

Air dry

Add 0.8 μl 20% CHCA

Air dry

Data Acquisition Settings

Energy absorbing molecule: 50% SPA

Set high mass to 100000 Daltons, optimized from 2000 Daltons to 100000Daltons.

Set starting laser intensity to 200.

Set starting detector sensitivity to 8.

Focus mass at 8000 Daltons.

Set Mass Deflector to 1000 Daltons.

Set data acquisition method to Seldi Quantitation

Set Seldi acquisition parameters 20. delta to 4. transients per to 10ending position to 80.

Set warming positions with 2 shots at intensity 225 (don't includewarming shots).

Process sample.

Measurement and Analysis of Biomarker Peak Intensities

Significance Analysis for Microarrays software (“SAM”) was used toidentify a set of significant peaks. SAM is described in detail inTusher et al. (PNAS (2001) 98: 5116-5121). The results of the SAManalysis are presented in FIG. 2. FIG. 2 shows that 11 out of 1619biomarkers were identified as differing significantly between the PADand non-PAD groups.

FIG. 3 shows a table that summarizes data relating to the 11 proteinsidentified in the SAM analysis discussed above, including the chip andQHyper DF fraction in which the protein was found. As indicated in theboxed area of the table of FIG. 3, 6 of the 11 proteins matched the massspectrometry fingerprint of beta 2-microglobulin. Experimental molecularweights are calculated based on the mass spectrometric data, while theCalculated molecular weights in the table are determined from the knownamino acid composition of beta 2-microglobulin and, where indicated, themass of an SPA adduct. Table 2 also summarizes the data relating to the11 significant proteins in FIG. 2, and includes the exact p values oflinear regression analysis between ABI values and peak intensities forthe selected differentially expressed proteins. TABLE 2 Exact P valuesof linear regresson between the peak intesitis and the ABI for selecteddifferentially expressed proteins in discovery data sets Mean PeakIntensities Correlation with ABI m/Z Conditions Putative protein PADControl P value rs P 11,732 CM10-pH7 Beta-2-microglobulin 2.842 ±0.1672.028 ±0.116 <0.001 −0.491 <0.001 11,731 CM10-pH7 Beta-2-microglobulin9.902 ±0.489 7.769 ±0.454 0.002 −0.419 <0.001 11,722 IMAC30-pH7Beta-2-microglobulin, most likely 11.367 ±0.716 8.644 ±0.588 0.004−0.366 0.001 11,731 IMAC30-pH5 Beta-2-microglobulin 13.883 ±1.613 7.543±0.484 <0.001 −0.401 <0.001 11,811 IMAC30-pH9 Beta-2-microglobulin, mostlikely 24.915 ±0.883 21.214 ±0.928 0.005 −0.287 0.007 11,941 IMAC30-pH5Beta-2-microglobulin, SPA addu

3.943 ±0.353 2.543 ±0.136 0.001 −0.410 <0.001 13,339 CM10-pH9 Cystatin C4.802 ±0.291 3.662 ±0.144 0.001 −0.330 0.002 14,690 CM10-pH9 Lysozyme C2.975 ±0.201 2.152 ±0.147 0.02 −0.374 <0.001 22,519 CM10-pH7 most likelyIgG light chain 2.809 ±0.119 2.211 ±0.138 <0.001 −0.504 <0.001 22,999CM10-organic Not determined 6.576 ±0.311 5.3852 ±0.247 0.004 −0.3510.001 36,067 CM10-pH4 Not determined 0.477 ±0.036 0.353 ±0.016 0.003−0.225 0.037

11.2. Western Blot Analysis of Beta 2-microglobulin Overexpression inPAD Patients

Using western blot and an anti-beta2 microglobulin antibody, higher beta2-microglobulin concentrations were observed in samples from 4 patientswith PAD compared to samples from 4 control subjects. The results areshown in FIG. 4. This finding is consistent when using plasmafractionated at pH 5 or using whole, unfractionated plasma.

11.3. Confirmation Study

A confirmation study was conducted to confirm that the observedcorrelation was not confounded by other patient traits (e.g., othercardiovascular risk factors, renal function, etc.). For thisconfirmation study, plasma was obtained from 20 patients with PAD and 20control subjects who had no clinical evidence of PAD or coronarydisease. FIG. 5 shows a table which summarizes the patients in the twogroups. The patients in the two comparison groups were similar in ageand gender. However, as expected, the PAD group had higher frequenciesof cardiovascular risk factors and a trend toward lower glomerularfiltration rate.

All plasma was obtained from patients in the fasting state. Beta2-microglobulin was measured using a commercially available ELISA kit.FIG. 6 shows that plasma and serum beta 2-microglobulin levels weresignificantly higher in PAD patients than control subjects, using aMann-Whitney nonparametric test.

FIG. 7 shows the results of a Spearman's Rank Correlation analysis. Theresults show that there is a strong negative correlation (r<−0.5)between beta 2-microglobulin levels and ABI. A relationship was alsoobserved between peak intensity of β2 m and claudication time, as shownin FIG. 10 (ACT; groups being normal subjects without claudication; PADwith absolute ACT >12 minutes; or PAD with ACT <12 minutes).

A linear regression analysis of the data showed that, among traditionalrisk factors for cardiovascular disease, a history of smoking,hyperlipidemia, and diabetes were statistically significant univariatepredictors of ankle-brachial index. In addition, glomerular filtrationrate had a positive trend toward a correlation with ankle-brachialindex. Beta 2-microglobulin, transformed logarithmically to reduceskewness, was strongly correlated with ankle-brachial index. The resultsare summarized in FIG. 8.

Using the listed variables in FIG. 8 with significance values less than0.2, a multivariate model was created to assess the independentrelationship between log beta 2-microglobulin and ankle-brachial index.This analysis confirms an independent relationship between high beta2-microglobulin levels and a lower ankle-brachial index, as shown inFIG. 9. Specifically, the results show that log beta 2-microglobulinlevels remain independently associated in an inverse manner with theankle-brachial index, even after adjustment for the potentialconfounding effects of lower glomerular filtration rates in PADpatients. This model predicted an estimated 45% of the variance ofankle-brachial index observed this study.

11.4. Validation Study in a Population at Risk for PAD

In patients undergoing coronary angiography, without known PAD status(n=237), serum β2 m was higher in patients with PAD. ABI was determinedprior to a comprehensive clinical characterization which includedquestionnaires to elicit demographics, ethnicity, quality of life,functional capacity; venipuncture for plasma, serum and genomic DNA; andcoronary angiography. Patients with PAD had an ABI at rest of <0.90, orin those with non-compressible ankle arteries, a toe-brachial index of<0.60. Glomerular filtration rate (GFR) was estimated by theModification of Diet in Renal Disease Study (MDRD) method 14.Beta-2-microglobulin levels correlated with ABI independent of othervascular risk factors and GFR by multivariate regression analysis (Table3). TABLE 3 Multivariate Regression for Predictors of Index ABI (n =273) Dependent Variable: Index Ankle Brachial Index SignificantIndependent Correlations Bolded Coefficient Standard Error p valueConstant 1.4030 0.1569 <0.001 Age −0.0012056 0.0002556 0.010 Diabetes−0.05948 0.02738 <0.001 History of Smoking −0.05948 0.02738 0.031Hypertension 0.0000321 0.0003223 0.921 Hyperlipidemia −0.00005770.0002753 0.834 Gender 0.0000352 0.0002369 0.882 Body Mass Index0.002556 0.002749 0.354 GFR −0.0003144 0.0005261 0.551 Log β2M −0.0195350.008783 0.027

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims. All publications, patents, and patentapplications cited herein are hereby incorporated by reference in theirentirety for all purposes.

What is claimed is:
 1. A method for qualifying peripheral artery diseasestatus in a subject comprising: (a) measuring β2-microglobulin in abiological sample from the subject; and (b) correlating the measurementor measurements with peripheral artery disease versus non-peripheralartery disease.
 2. The method of claim 1, comprising measuring aplurality of biomarkers in the biological sample in addition toβ2-microglobulin.
 3. The method of claim 2, wherein said plurality ofadditional biomarkers includes at least one biomarker selected from thegroup consisting of the biomarkers lysozyme and cystatin C.
 4. Themethod of claim 2, wherein at least β2-microglobulin and lysozyme aremeasured.
 5. The method of claim 2, wherein at least β2-microglobulinand cystatin C are measured.
 6. The method of claim 2, wherein at leastβ2-microglobulin, cystatin C and lysozyme are measured.
 7. The method ofclaim 2, wherein three biomarkers are measured, and wherein said threebiomarkers are β2-microglobulin, cystatin C and lysozyme.
 8. The methodof claim 1, wherein β2-microglobulin is measured by mass spectrometry.9. The method of claim 1, wherein mass spectrometry is SELDI-MS.
 10. Themethod of claim 1, wherein β32-microglobulin is measured by a methodother than mass spectrometry.
 11. The method of claim 1, whereinβ2-microglobulin is measured by immunoassay.
 12. The method of any ofclaims 2-7, wherein at least one of the biomarkers selected from thegroup consisting of β2-microglobulin, cystatin C and lysozyme ismeasured by immunoassay.
 13. The method of claim 1, wherein the sampleis blood or a blood derivative.
 14. The method of claim 1, wherein thecorrelating is performed by executing a software classificationalgorithm.
 15. The method of claim 1, further comprising: (c) reportingthe status to the subject.
 16. The method of claim 1, furthercomprising: (c) recording the status on a tangible medium.
 17. Themethod of claim 1, further comprising: (c) managing subject treatmentbased on the status.
 18. The method of claim 17, further comprising: (d)measuring the at least one biomarker after subject management andcorrelating the measurement with disease progression.
 19. A method fordetermining the course of peripheral artery disease comprising: (a)measuring, at a first time, β2-microglobulin in a biological sample fromthe subject; (b) measuring, at a second time, β2-microglobulin in abiological sample from the subject; and (c) comparing the firstmeasurement and the second measurement; wherein the comparativemeasurements determine the course of the peripheral artery disease. 20.A kit comprising: (a) a solid support comprising at least one capturereagent attached thereto, wherein the capture reagent bindsβ2-microglobulin; and (b) instructions for using the solid support todetect β2-microglobulin.
 21. The kit of claim 20, wherein the solidsupport comprising a capture reagent is a SELDI probe.
 22. The kit ofclaim 20, further comprising a standard reference of β2-microglobulin.23. A software product comprising: a. code that accesses data attributedto a sample, the data comprising measurement of at least one biomarkerin the sample, wherein at least one biomarker is beta-2-microglobulin;and b. code that executes a classification algorithm that classifies theperipheral artery disease status of the sample as a function of themeasurement.
 24. A method comprising communicating to a subject adiagnosis relating to peripheral artery disease status determined fromthe correlation of at least one biomarker in a sample from the subject,wherein at least one biomarker is beta-2-microglobulin.
 25. The methodof claim 25, wherein the diagnosis is communicated to the subject via acomputer-generated medium.
 26. A method for identifying a compound thatinteracts with beta-2-microglobulin, wherein said method comprises: a)contacting beta-2-microglobulin with a test compound; and b) determiningwhether the test compound interacts with beta-2-microglobulin.
 27. Amethod for qualifying peripheral artery disease status in a subjectcomprising: (a) measuring cystatin C in a biological sample from thesubject; and (b) correlating the measurement or measurements withperipheral artery disease versus non-peripheral artery disease.
 28. Amethod for qualifying peripheral artery disease status in a subjectcomprising: (a) measuring lysozme in a biological sample from thesubject; and (b) correlating the measurement or measurements withperipheral artery disease versus non-peripheral artery disease.
 29. Themethod of claim 27 or 28, comprising measuring a plurality of biomarkersin the biological sample, wherein at least one of the additionalbiomarkers is β2-microglobulin.
 30. A method for qualifying peripheralartery disease status in a subject comprising: (a) measuring at leastone biomarker in a biological sample from the subject, wherein said atleast one biomarker is selected from the group consisting ofβ2-microglobulin, lysozyme and cystatin C; and (b) further measuring oneor more of the following criteria of said subject: C-reactive proteinlevels, total cholesterol levels, triglyceride levels, low densitylipoprotein levels, high density lipoprotein levels, homocysteinelevels, interleukin levels, fibrinogen levels, lipoprotein A levels,8-iso-prostaglandin F 2alpha (8-iso-PGF 2alpha) levels, and soluble Faslevels; and (c) correlating said measurements (a) and (b) withperipheral artery disease versus non-peripheral artery disease.
 31. Amethod for predicting a subject's responsiveness to a therapeuticregimen for treating peripheral artery diseases, comprising: (a) firstmeasuring at least one biomarker in a biological sample from thesubject, wherein said at least one biomarker is selected from the groupconsisting of β2-microglobulin, lysozyme and cystatin C; and (b) aftersaid first measuring step, administering an initial treatment in atherapeutic regimen for treating peripheral artery disease; and (c)after said treatment, measuring said at least one biomarker a secondtime; and (d) comparing said first and second measurements, whereindecreasing levels of said at least one biomarker correlate with anincreased likelihood of a subject's responsiveness to said therapeuticregimen.
 32. The method of any of claims 27-31, wherein said measurementor measurements of a biomarker is performed using an immunoassay.